It is both common and useful to collect large amounts of data over a long period of time for a wide variety of applications. For example, data tracking stock prices, ozone levels, room temperatures, server usage, etc. can be collected and analyzed for any number of reasons—both personal and professional. However, as the number of data points grows, the sheer volume of data can make viewing the data difficult and cumbersome. Typically, large time series data is presented in a single resolution, linear fashion. For example, the fluctuations in a stock price over time are often presented by plotting evenly dispersed time points against stock price in a X-Y graph.
If, as often happens, there are more data points than can reasonably be displayed in the allocated display space (e.g. a sheet of paper, a window on a computer screen), the user is typically provided with two options: first, the data is maintained at the same resolution and the display space is enlarged (e.g. by adding another sheet of paper or by increasing the size of the window and providing the user with scroll bars to move around the window); or second, the data is provided at reduced-resolution (e.g. 1 month's worth of data may show data points at 1-day intervals while a year's worth of data may show data points at 1-week intervals.)
However, forcing users to switch between different layouts at different resolutions is cumbersome and often makes it difficult to maintain data in context. Accordingly, it would be desirable to provide improved methods and display systems for viewing long time series data in a manageable and convenient way.